


"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 	lowercase__				(					unittest.TestCase				):
   __UpperCAmelCase							       =					ViTImageProcessor if is_vision_available() else None





   @property
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[str]   =		(3, 32, 128)
      _lowerCamelCase	:					List[Any]   =		tempfile.mkdtemp()

      # fmt: off
      _lowerCamelCase	:					Optional[int]   =		["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _lowerCamelCase	:					Dict   =		dict(zip(SCREAMING_SNAKE_CASE      ,   range(len(SCREAMING_SNAKE_CASE))))

      _lowerCamelCase	:					int   =		os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["""vocab_file"""])
      with open(self.vocab_file      ,   """w"""      ,   encoding="""utf-8""") as fp:
         fp.write(json.dumps(SCREAMING_SNAKE_CASE) + """\n""")

      _lowerCamelCase	:					Any   =		{
          """do_normalize""": False,
          """do_resize""": True,
          """image_processor_type""": """ViTImageProcessor""",
          """resample""": 3,
          """size""": {"""height""": 32, """width""": 128},
      }
      _lowerCamelCase	:					Union[str, Any]   =		os.path.join(self.tmpdirname      ,   SCREAMING_SNAKE_CASE)
      with open(self.image_processor_file      ,   """w"""      ,   encoding="""utf-8""") as fp:
         json.dump(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return ViTImageProcessor.from_pretrained(self.tmpdirname      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      shutil.rmtree(self.tmpdirname)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		np.random.randint(255      ,   size=(3, 30, 400)      ,   dtype=np.uinta)

      _lowerCamelCase	:					Union[str, Any]   =		Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE      ,   0      ,   -1))

      return image_input





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[Any]   =		self.get_tokenizer()
      _lowerCamelCase	:					Dict   =		self.get_image_processor()

      _lowerCamelCase	:					Tuple   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)
      processor.save_pretrained(self.tmpdirname)
      _lowerCamelCase	:					Dict   =		MgpstrProcessor.from_pretrained(self.tmpdirname      ,   use_fast=SCREAMING_SNAKE_CASE)

      self.assertEqual(processor.char_tokenizer.get_vocab()      ,   tokenizer.get_vocab())
      self.assertIsInstance(processor.char_tokenizer      ,   SCREAMING_SNAKE_CASE)

      self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor.to_json_string())
      self.assertIsInstance(processor.image_processor      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer()
      _lowerCamelCase	:					Any   =		self.get_image_processor()

      _lowerCamelCase	:					List[Any]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)
      processor.save_pretrained(self.tmpdirname)

      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer(bos_token="""(BOS)"""      ,   eos_token="""(EOS)""")
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      _lowerCamelCase	:					Any   =		MgpstrProcessor.from_pretrained(
          self.tmpdirname      ,   bos_token="""(BOS)"""      ,   eos_token="""(EOS)"""      ,   do_normalize=SCREAMING_SNAKE_CASE      ,   padding_value=1.0)

      self.assertEqual(processor.char_tokenizer.get_vocab()      ,   tokenizer_add_kwargs.get_vocab())
      self.assertIsInstance(processor.char_tokenizer      ,   SCREAMING_SNAKE_CASE)

      self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string())
      self.assertIsInstance(processor.image_processor      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					Optional[int]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Optional[Any]   =		image_processor(SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")
      _lowerCamelCase	:					Tuple   =		processor(images=SCREAMING_SNAKE_CASE      ,   return_tensors="""np""")

      for key in input_image_proc.keys():
         self.assertAlmostEqual(input_image_proc[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Dict   =		self.get_image_processor()
      _lowerCamelCase	:					str   =		self.get_tokenizer()

      _lowerCamelCase	:					Dict   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		"""test"""

      _lowerCamelCase	:					Union[str, Any]   =		processor(text=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		tokenizer(SCREAMING_SNAKE_CASE)
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key])





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Optional[int]   =		self.get_tokenizer()

      _lowerCamelCase	:					Any   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		"""test"""
      _lowerCamelCase	:					Dict   =		self.prepare_image_inputs()

      _lowerCamelCase	:					Tuple   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

      self.assertListEqual(list(inputs.keys())      ,   ["""pixel_values""", """labels"""])

      # test if it raises when no input is passed
      with pytest.raises(SCREAMING_SNAKE_CASE):
         processor()





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Tuple   =		self.get_image_processor()
      _lowerCamelCase	:					Union[str, Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					Union[str, Any]   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCamelCase	:					Dict   =		processor.char_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		[seq.replace(""" """      ,   """""") for seq in decoded_tok]

      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase	:					Optional[int]   =		self.get_image_processor()
      _lowerCamelCase	:					Dict   =		self.get_tokenizer()

      _lowerCamelCase	:					int   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					str   =		None
      _lowerCamelCase	:					List[str]   =		self.prepare_image_inputs()

      _lowerCamelCase	:					List[Any]   =		processor(text=SCREAMING_SNAKE_CASE      ,   images=SCREAMING_SNAKE_CASE)

      self.assertListEqual(list(inputs.keys())      ,   processor.model_input_names)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Optional[Any]   =		self.get_image_processor()
      _lowerCamelCase	:					Optional[Any]   =		self.get_tokenizer()

      _lowerCamelCase	:					int   =		MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE      ,   image_processor=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		torch.randn(1      ,   27      ,   38)
      _lowerCamelCase	:					int   =		torch.randn(1      ,   27      ,   5_0257)
      _lowerCamelCase	:					Dict   =		torch.randn(1      ,   27      ,   3_0522)

      _lowerCamelCase	:					Union[str, Any]   =		processor.batch_decode([char_input, bpe_input, wp_input])

      self.assertListEqual(list(results.keys())      ,   ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()